1. Field of the Invention
The present invention relates to an internal combustion gasoline engine having a fuel injection control system, and more particularly to a control system which controls an air-fuel ratio of the engine.
2. Description of the Prior Art
A prior air-fuel ratio control of the engine has been usually a PID feed back control using an O.sub.2 sensor or an air-fuel ratio sensor. It has been successfully performed in a stationary operating state such as during dead time. It performs fuel increase correction and decrease correction in a transient state such as during acceleration and deceleration. However, a part of injected fuel drops out of air-fuel mixture to stick to the wall of an intake manifold and an intake valve, and a part of the sticking fuel evaporates to rejoin the mixture. Therefore, it can not control the air-fuel ratio at an exact target value in the transient state such as during acceleration and deceleration.
On the other hand, techniques have been proposed to create a model that incorporates the sticking fuel. For example, Japanese Pat. Kokoku Hei 5-73908 defined the sticking rate that the injected fuel sticks to the valve and the wall, and an evaporation time constant to correct the air-fuel ratio. FIG. 16 illustrates an example of such modeling, which is the manifold fuel transport model of Japanese Pat. Kokoku Hei. 5-73908.
In FIG. 16, Gf denotes the amount of injected fuel, X denotes the fuel sticking rate, Mf denotes the liquid film amount that is the total amount of fuel that is sticking to the manifold wall and the like, .tau. denotes the evaporation time constant, and Gfe denotes the cylinder-flowing fuel amount that is the actual amount of fuel that flows into the cylinder. In this model, these variables and parameters are related in the following equations. EQU dMf/dt=-Mf/.tau.+XGf EQU Gfe=Mf/.tau.+(1-X)Gf
However, the evaporation time constant and the sticking rate are determined by a great number of complex factors such as the amount of the air passing through the manifold, the temperature of the manifold, the quality of fuel, and individual deviations. Therefore, it is hard to obtain the values of these parameters. For example, we must apply a method of creating a matrix data group of parameters by obtaining step responses to fuel input under various conditions of the running engine. Further, in order to match the model with an actual engine, it is necessary to minimize the deviations (errors) of simulated responses for the responses of the actual engine. Consequently, a great amount of time and a great number of modifications are necessary in order to implement the model to control the engine at a target air-fuel ratio.
As described above, prior approaches, using whether the PID control or a sticking-fuel model, must first determine the structure and parameters of the engine that affect air-fuel ratios. The data of the parameters must be obtained beforehand by experiments and simulation, and must be swapped depending on operating conditions. The experiments and simulation should be repeated and the amount of the data should be increased to control air-fuel ratios with great accuracy.
Apart from the sticking fuel, the following are among the factors for varying air-fuel ratios:
(1) A delay determined by a time delay between the detection time of the O.sub.2 sensor or air-fuel ratio sensor and the injection time of the fuel. PA1 (2) A delay due to electric processing of the intake air signal. PA1 (3) A delay in detecting changes in throttle position. PA1 (4) A control delay due to an acceleration judgment delay. PA1 (5) A delay in mechanical opening of the injector and a delay in the fuel flow. PA1 (6) A delay determined by a shift in fuel injection timing and by the timing of opening and closing the intake valve. PA1 (7) Control deviations (errors) due to thermal response delays.
Therefore, the structure of the engine is complex, so that modeling is hard, and a great number of data maps are necessary. In particular, concerning the delay of (1), the transport lag between the exhaust valve and the location of the sensor is great, and the amount of the lag varies depending on operating conditions (the engine speed, the intake manifold pressure, and the like). Therefore, the dead time of the controlled system is time-dependent, so that performing the feedback control stably and accurately is very hard.
As an approach to resolve these difficulties, there is a neural network model that learns non-linear factors such as the fuel sticking of the engine to directly compute the amount of injected fuel. The aim of this approach is to shorten the time for adjusting the parameters and, at the same time, to improve the response characteristics in the transient state. However, accuracy in learning and universality in control performance are in a trade-off relation. In fact, the selection of data is hard to make the neural network learn for the whole operating range, and a huge size of a data group is necessary as a result. Further, the amount of injected fuel, which is the control input to the controlled system, is directly computed, so that the stability of the control system is not guaranteed. No logical method of changing the control characteristics is available in this model, except determining the values of control variables by trial and error of repetitive learning.